GeezerVison

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# An introduction to solving biological problems with Python\n", "\n", "- Our course webpage: http://pycam.github.io\n", "- Python website: https://www.python.org/ \n", "- [Python 3 Standard Library](https://docs.python.org/3/library/index.html])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Learning objectives\n", "\n", "- **Recall** what we've learned so far on variables, common data types and collections\n", "- **Propose and create** solutions using these concepts in an exercise\n", "- **Use** conditions to execute specific code block\n", "- **Employ** loops to repeat code block\n", "- **Practice** reading and writing files with Python\n", "- **Solve** more complex exercises" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Course schedule - day two\n", "\n", "- 09:30-09:45: [0h15] **Introduction**\n", "- 09:45-10:45: [1h00] **Session 2.1** - Conditional execution\n", "- 10:45-11:00: *break*\n", "- 11:00-12:30: [1h30] **Session 2.2** - Loops\n", "- 12:30-13:30: *lunch break*\n", "- 13:30-15:00: [1h30] **Session 2.3** - Files\n", "- 15:00-15:15: *break*\n", "- 15:15-16:15: [1h00] **Session 2.4** - Delimited files\n", "- 16:15-16:30: *break*\n", "- 16:30-17:00: [0h30] **Wrap-up**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## What we've learned so far\n", "\n", "- Simple data types, Collections\n", "- Functions used so far..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Simple data types" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Integer: 1\n", "Float 3.14\n", "True\n" ] } ], "source": [ "## Integer\n", "i = 1\n", "print('Integer:', i)\n", "## Float\n", "x = 3.14\n", "print('Float', x)\n", "## Boolean\n", "print(True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ATGTCGTCTACAACACTspam's\n", "ATGTCGTCTACAACACT spam's\n" ] } ], "source": [ "## String\n", "s0 = '' # empty string\n", "s1 = 'ATGTCGTCTACAACACT' # single quotes\n", "s2 = \"spam's\" # double quotes\n", "print(s1 + s2) # concatenate\n", "print(s1, s2) # print" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Collections" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A tuple: (2, 3, 4, 5)\n", "First element of tuple: 2\n" ] } ], "source": [ "## Tuple - immutable\n", "my_tuple = (2, 3, 4, 5)\n", "print('A tuple:', my_tuple)\n", "print('First element of tuple:', my_tuple[0])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A list: [2, 3, 4, 5]\n", "First element of list: 2\n", "Appended list: [2, 3, 4, 5, 12]\n", "Modified list: [45, 3, 4, 5, 12]\n" ] } ], "source": [ "## List\n", "my_list = [2, 3, 4, 5]\n", "print('A list:', my_list)\n", "print('First element of list:', my_list[0])\n", "my_list.append(12)\n", "print('Appended list:', my_list)\n", "my_list[0] = 45\n", "print('Modified list:', my_list)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Here is a string: ATGTCATTT\n", "First character: A\n", "Slice text[1:3]: TG\n", "Number of characters in text 9\n" ] } ], "source": [ "## String - immutable, tuple of characters\n", "text = \"ATGTCATTT\"\n", "print('Here is a string:', text)\n", "print('First character:', text[0])\n", "print('Slice text[1:3]:', text[1:3])\n", "print('Number of characters in text', len(text))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A set: {1, 2, 4, 5, 6}\n" ] } ], "source": [ "## Set - unique unordered elements\n", "my_set = set([1,2,2,2,2,4,5,6,6,6])\n", "print('A set:', my_set)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A dictionary: {'A': 'Adenine', 'C': 'Cytosine', 'G': 'Guanine', 'T': 'Thymine'}\n", "Value associated to key C: Cytosine\n" ] } ], "source": [ "## Dictionary\n", "my_dictionary = {\"A\": \"Adenine\", \n", " \"C\": \"Cytosine\", \n", " \"G\": \"Guanine\", \n", " \"T\": \"Thymine\"}\n", "print('A dictionary:', my_dictionary)\n", "print('Value associated to key C:', my_dictionary['C'])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Functions used so far..." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 5 elements in the list ['A', 'C', 'A', 'T', 'G']\n", "There are 2 letter A in the list ['A', 'C', 'A', 'T', 'G']\n", "['ATG', 'TCA', 'CCG', 'GGC']\n" ] } ], "source": [ "my_list = ['A', 'C', 'A', 'T', 'G']\n", "print('There are', len(my_list), 'elements in the list', my_list)\n", "print('There are', my_list.count('A'), 'letter A in the list', my_list)\n", "print(\"ATG TCA CCG GGC\".split())" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Next session\n", "\n", "Go to our next notebook: [python_basic_2_1](python_basic_2_1.ipynb)" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }